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Top 14 Recommender System Open-Source Projects
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annoy
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy.
Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy) has been around since 2014. It was one of the first open source approximate nearest neighbor libraries. Recommendations have always been a good use case for vector similarity.
Recommendations are a natural extension of search and transformers models made building the vectors for natural language possible. To prove the worth of vector search over keyword search, the focus was always on showing how the top N matches include results not possible with keyword search.
In 2023, there has been a shift towards acknowledging keyword search also has value and that a combination of vector + keyword search (aka hybrid search) operates in the sweet spot. Once again this is validated through the same benchmarks which focus on the top 10.
On top of all this, there is also the reality that the vector database space is very crowded and some want to use their performance benchmarks for marketing.
Disclaimer: I am the author of txtai (https://github.com/neuml/txtai), an open source embeddings database
Project mention: RecBole – A unified, comprehensive and efficient recommendation library | news.ycombinator.com | 2024-01-17
Ranx is a great library for mixing results from different sources.
Project mention: Graph Masked Autoencoder for Sequential Recommendation | /r/BotNewsPreprints | 2023-05-09While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective graph masked autoencoder that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/GMRec.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Recommender Systems related posts
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RecBole – A unified, comprehensive and efficient recommendation library
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Vector Databases 101
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I'm an undergraduate data science intern and trying to run kmodes clustering. Did this elbow method to figure out how many clusters to use, but I don't really see an "elbow". Tips on number of clusters?
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Calculating document similarity in a special domain
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Can Parquet file format index string columns?
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[D]: Best nearest neighbour search for high dimensions
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Billion-Scale Approximate Nearest Neighbor Search [pdf]
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A note from our sponsor - InfluxDB
www.influxdata.com | 2 May 2024
Index
What are some of the best open-source Recommender System projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | annoy | 12,712 |
| 2 | DeepLearningExamples | 12,642 |
| 3 | implicit | 3,427 |
| 4 | RecBole | 3,181 |
| 5 | spotlight | 2,934 |
| 6 | ranking | 2,714 |
| 7 | libffm | 1,594 |
| 8 | TensorRec | 1,258 |
| 9 | fastFM | 1,063 |
| 10 | NeuRec | 1,031 |
| 11 | goodbooks-10k | 794 |
| 12 | ranx | 344 |
| 13 | MAERec | 50 |
| 14 | reco-model-monitoring | 3 |
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